Proliferating fibroblasts on 0020227 for Chris
Environment
library(Seurat)Loading required package: SeuratObject
Loading required package: sp
‘SeuratObject’ was built under R 4.3.0 but the current version is 4.3.2; it is recomended that you reinstall ‘SeuratObject’ as the ABI for R
may have changed
Attaching package: ‘SeuratObject’
The following object is masked from ‘package:base’:
intersectlibrary(tidyverse)── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.3 ✔ tidyr 1.3.1
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✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorsRead in Xenium
ipf3.xen <- LoadXenium('output-XETG00143__0020227__Region_1__20231214__022306/')10X data contains more than one type and is being returned as a list containing matrices of each type.
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Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')General processing on 0020227
Filter
On the theory that if there was only few features, but they had counts, it is probably still interpretable.
ipf3.filtered.xen <- subset(ipf3.xen, subset=nCount_Xenium>20 & nFeature_Xenium>4)Warning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating Seurat objectsprint('original dataset:')[1] "original dataset:"print(ncol(ipf3.xen))[1] 580730print('filtered dataset:')[1] "filtered dataset:"print(ncol(ipf3.filtered.xen))[1] 158303# cleanup cleanup
rm(ipf3.xen)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 12227670 653.1 85557660 4569.3 106947074 5711.6
Vcells 2224641624 16972.7 4452581310 33970.5 3805168063 29031.2Standard Processing
DefaultAssay(ipf3.filtered.xen) <- 'Xenium'
ipf3.filtered.xen <- NormalizeData(ipf3.filtered.xen, verbose=F)
ipf3.filtered.xen <- FindVariableFeatures(ipf3.filtered.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.filtered.xen <- ScaleData(ipf3.filtered.xen, verbose=F)
ipf3.filtered.xen <- RunPCA(ipf3.filtered.xen, npcs = 30, features = rownames(ipf3.xen), verbose=F)Error: object 'ipf3.xen' not foundModified Clustering
Modification to reduce cluster number. i think the sparsity of data means that pruned edges leads to more communities. Major change is the prune.SNN parameter.
ipf3.filtered.xen <- FindNeighbors(ipf3.filtered.xen, reduction='pca', dims=1:30, prune.SNN=0) Computing nearest neighbor graph
Computing SNNipf3.filtered.xen <- FindClusters(ipf3.filtered.xen, resolution = 0.6)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 158303
Number of edges: 34314140
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8555
Number of communities: 16
Elapsed time: 353 secondsDimPlot(ipf3.filtered.xen, label=T)Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Proliferation (general)
10X core set includes: CCNA1, CCNB2, CDK1, CENPF, KIT, MKI67, PCNA, TOP2A.
FeaturePlot(ipf3.filtered.xen, features=c('COL1A1','CTHRC1','SCN7A','PLA2G2A'),
min.cutoff='q25', max.cutoff='q90', order=T)Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
FeaturePlot(ipf3.filtered.xen, features=c('RGS5','MYH11','CNN1','SFRP2'),
min.cutoff='q25', max.cutoff='q90', order=T)Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
FeaturePlot(ipf3.filtered.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
min.cutoff='q25', max.cutoff='q90', order=T)Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
FeaturePlot(ipf3.filtered.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
min.cutoff='q25', max.cutoff='q90', order=T)Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
p <- DotPlot(ipf3.filtered.xen, features=c('EPCAM','ITGB6',
'COL1A1','CTHRC1','SCN7A','PLA2G2A','RGS5','MFAP5','PI16','MYH11','CNN1','SFRP2',
'CCNA1','CCNB2','CDK1','CENPF','KIT','MKI67','PCNA','TOP2A'))
p + coord_flip() + scale_x_discrete(limits = rev)
ImageFeaturePlot(ipf3.filtered.xen, features=c('ARG1'),
min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
ImageFeaturePlot(ipf3.filtered.xen, features=c('IL13'),
min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
ImageFeaturePlot(ipf3.filtered.xen, features=c('IL6'),
min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
Fibroblast subset
ipf3.fib.xen <- subset(ipf3.filtered.xen, idents=c(0,5,9,15))Warning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating Seurat objectsDimPlot(ipf3.fib.xen, label=T)
As usual I think the cell cycle signal is strong enough that it forced all proliferative cells into cluster 15 across different cell types. Proceed and prune later. ## Recluster
ipf3.fib.xen <- NormalizeData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- FindVariableFeatures(ipf3.fib.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.fib.xen <- ScaleData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- RunPCA(ipf3.fib.xen, npcs = 30, features = rownames(ipf3.filtered.xen), verbose=F)
ipf3.fib.xen <- RunUMAP(ipf3.fib.xen, dims = 1:30, verbose=F)ipf3.fib.xen <- FindNeighbors(ipf3.fib.xen, reduction='pca', dims=1:30, prune.SNN=0) Computing nearest neighbor graph
Computing SNNipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 1)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 43496
Number of edges: 10770942
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6781
Number of communities: 11
Elapsed time: 68 secondsDimPlot(ipf3.fib.xen, label=T)
Those fucking proliferative cells, EPCAM+ and COL1+ lumped together in cluster 9. Really justifies why regression is useful. Try higher res first.
ipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 2)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 43496
Number of edges: 10770942
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5693
Number of communities: 21
Elapsed time: 58 secondsDimPlot(ipf3.fib.xen, label=T)
FeaturePlot(ipf3.fib.xen, features=c('EPCAM','ITGB6','ABCA3','KRT8'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('COL1A1','CTHRC1','SCN7A','PLA2G2A'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('RGS5','MYH11','CNN1','SFRP2'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
min.cutoff='q25', max.cutoff='q90', order=T)
Whew. Drop cluster 19 and 7, these got roped in here probably because they are DATPs that express some collagen or proliferative epithelium. One is almost certainly AT2 (ABCA3/KRT8/ITGB6 positive) and the other is proliferative epithelial.
ipf3.fib.xen <- subset(x = ipf3.fib.xen, idents = c(7,19), invert = TRUE)Warning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating Centroids objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating FOV objectsWarning: Not validating Seurat objectsRecluster after pruning
ipf3.fib.xen <- NormalizeData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- FindVariableFeatures(ipf3.fib.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.fib.xen <- ScaleData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- RunPCA(ipf3.fib.xen, npcs = 30, features = rownames(ipf3.filtered.xen), verbose=F)
ipf3.fib.xen <- RunUMAP(ipf3.fib.xen, dims = 1:30, verbose=F)ipf3.fib.xen <- FindNeighbors(ipf3.fib.xen, reduction='pca', dims=1:30, prune.SNN=0, verbose=F)
ipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 1.5, verbose=F)DimPlot(ipf3.fib.xen, label=T)
FeaturePlot(ipf3.fib.xen, features=c('COL1A1','CTHRC1','POSTN','SCN7A'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('INMT','RGS5','MYH11','CNN1'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('SFRP2','PLA2G2A','MFAP5','PI16'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('LGR5','WNT5A'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('LTBP2','COL5A2','COL8A1','DPP6'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('APOD','MFAP5','FBN1','FGFR4'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('PCOLCE2','PDGFRA','SFRP2','SVEP1'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('WNT2','MEDAG','PIM1','RARRES1'),
min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('THBS2'),
min.cutoff='q25', max.cutoff='q90', order=T)
p <- DotPlot(ipf3.fib.xen, features=unique(c('EPCAM','ITGB6',
'COL1A1','CTHRC1','POSTN','COL8A1','SCN7A','INMT','RGS5','MYH11','CNN1','SFRP2',
'PLA2G2A','MFAP5','PI16','LGR5','WNT5A','CCNA1','CCNB2','CDK1','CENPF',
'KIT','MKI67','PCNA','TOP2A','LTBP2','COL5A2','COL8A1','DPP6',
'APOD','MFAP5','FBN1','FGFR4','PCOLCE2','PDGFRA','SFRP2','SVEP1',
'WNT2','MEDAG','PIM1','RARRES1','THBS2'))
)
p + coord_flip() + scale_x_discrete(limits = rev)
ipf3.fib.xen <- RenameIdents(object = ipf3.fib.xen,
`0` = "SmoothMuscle",
`1` = "AlveolarFib",
`2` = "FibroticFib_CTHRC1low",
`3` = "Pericyte",
`4` = "LipoFib",
`5` = "SmoothMuscle",
`6` = "AlveolarFib",
`7` = "FibroticFib_CTHRC1high",
`9` = "ProliferatingFib",
`8` = "AdventitialFib",
`10` = "AlveolarFib",
`11` = "Pericyte",
`12` = "AlveolarFib",
`13` = "AlveolarFib"
)
DimPlot(ipf3.fib.xen, label=T)
Marker learning
On the theory that there might be hidden markers of utility in the Xenium probeset that we could co-opt for next cycle.
fib.markers <- FindAllMarkers(ipf3.fib.xen, only.pos=T)Calculating cluster SmoothMuscle
For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the presto package
--------------------------------------------
install.packages('devtools')
devtools::install_github('immunogenomics/presto')
--------------------------------------------
After installation of presto, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster FibroticFib_CTHRC1high
| | 0 % ~calculating
|++ | 3 % ~02s
|+++ | 6 % ~02s
|+++++ | 8 % ~02s
|++++++ | 11% ~02s
|+++++++ | 14% ~02s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster ProliferatingFib
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~08s
|++++ | 7 % ~08s
|+++++ | 8 % ~08s
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|++++++++ | 15% ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster AdventitialFib
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s fib.markers %>% group_by(cluster) %>% slice_head(n=10) %>% ungroup() -> top10
top10# manually save this
p1 <- DoHeatmap(ipf3.fib.xen, features = top10$gene) + NoLegend()
ggsave('forChris-proliferation-on-0020227_files/figure-gfm/markers-heatmap.png', units='in', width=9, height=7)Generate assignments file for Xenium Explorer
For version 1.2.0, the instructons say:
“Visualize custom cells groups in Xenium Explorer by importing a CSV file specifying cell ids and corresponding group names. Build a CSV file containing each cell id in the first column and the group the cell should be assigned to in the second column. A cell may only be assigned to one group. Not all cells in the dataset need to be included.
Ensure the columns have headers named “cell_id” and “group”.”
This is basically the named_clusters list with a header and saved as a CSV.
scratch <- tibble(cell_id = names(ipf3.fib.xen$named_clusters),
group = ipf3.fib.xen$named_clusters)
write.csv(scratch, 'Seurat_fib_named_clusters.csv')---
title: "Proliferating fibroblasts on 0020227 for Chris"
output: 
  github_document:
    toc: true
  html_notebook:
    toc: true
---
# Environment
```{r}
library(Seurat)
library(tidyverse)
```
## Read in Xenium 
```{r}
ipf3.xen <- LoadXenium('output-XETG00143__0020227__Region_1__20231214__022306/')
```
# General processing on 0020227 
## Filter
On the theory that if there was only few features, but they had counts, it is probably still interpretable.
```{r}
ipf3.filtered.xen <- subset(ipf3.xen, subset=nCount_Xenium>20 & nFeature_Xenium>4)
```
```{r}
print('original dataset:')
print(ncol(ipf3.xen))
print('filtered dataset:')
print(ncol(ipf3.filtered.xen))
```
```{r}
# cleanup cleanup
rm(ipf3.xen)
gc()
```

## Standard Processing
```{r}
DefaultAssay(ipf3.filtered.xen) <- 'Xenium'
ipf3.filtered.xen <- NormalizeData(ipf3.filtered.xen, verbose=F)
ipf3.filtered.xen <- FindVariableFeatures(ipf3.filtered.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.filtered.xen <- ScaleData(ipf3.filtered.xen, verbose=F)
ipf3.filtered.xen <- RunPCA(ipf3.filtered.xen, npcs = 30, features = rownames(ipf3.filtered.xen), verbose=F)
ipf3.filtered.xen <- RunUMAP(ipf3.filtered.xen, dims = 1:30, verbose=F)
```
## Modified Clustering
Modification to reduce cluster number. i think the sparsity of data means that pruned edges leads to more communities. Major change is the prune.SNN parameter.
```{r}
ipf3.filtered.xen <- FindNeighbors(ipf3.filtered.xen, reduction='pca', dims=1:30, prune.SNN=0) 
ipf3.filtered.xen <- FindClusters(ipf3.filtered.xen, resolution = 0.6)
```
```{r}
DimPlot(ipf3.filtered.xen, label=T)
```
# Proliferation (general)
10X core set includes: CCNA1, CCNB2, CDK1, CENPF, KIT, MKI67, PCNA, TOP2A.
```{r}
FeaturePlot(ipf3.filtered.xen, features=c('COL1A1','CTHRC1','SCN7A','PLA2G2A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.filtered.xen, features=c('RGS5','MYH11','CNN1','SFRP2'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.filtered.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.filtered.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
```
```{r}
p <- DotPlot(ipf3.filtered.xen, features=c('EPCAM','ITGB6',
                                           'COL1A1','CTHRC1','SCN7A','PLA2G2A','RGS5','MFAP5','PI16','MYH11','CNN1','SFRP2',
                                      'CCNA1','CCNB2','CDK1','CENPF','KIT','MKI67','PCNA','TOP2A'))
p + coord_flip() + scale_x_discrete(limits = rev)
```
```{r}
ImageFeaturePlot(ipf3.filtered.xen, features=c('ARG1'),
            min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
ImageFeaturePlot(ipf3.filtered.xen, features=c('IL13'),
            min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
ImageFeaturePlot(ipf3.filtered.xen, features=c('IL6'),
            min.cutoff='q25', max.cutoff='q60', cols=c('white','red'))
```
# Fibroblast subset
```{r}
ipf3.fib.xen <- subset(ipf3.filtered.xen, idents=c(0,5,9,15))
DimPlot(ipf3.fib.xen, label=T)
```
As usual I think the cell cycle signal is strong enough that it forced all proliferative cells into cluster 15 across different cell types. Proceed and prune later.
## Recluster
```{r}
ipf3.fib.xen <- NormalizeData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- FindVariableFeatures(ipf3.fib.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.fib.xen <- ScaleData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- RunPCA(ipf3.fib.xen, npcs = 30, features = rownames(ipf3.filtered.xen), verbose=F)
ipf3.fib.xen <- RunUMAP(ipf3.fib.xen, dims = 1:30, verbose=F)
```
```{r}
ipf3.fib.xen <- FindNeighbors(ipf3.fib.xen, reduction='pca', dims=1:30, prune.SNN=0) 
ipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 1)
```
```{r}
DimPlot(ipf3.fib.xen, label=T)
```
Those fucking proliferative cells, EPCAM+ and COL1+ lumped together in cluster 9. Really justifies why regression is useful. Try higher res first.
```{r}
ipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 2)
DimPlot(ipf3.fib.xen, label=T)
```


```{r}
FeaturePlot(ipf3.fib.xen, features=c('EPCAM','ITGB6','ABCA3','INMT'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('COL1A1','CTHRC1','SCN7A','PLA2G2A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('RGS5','MYH11','CNN1','SFRP2'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
```
Whew. Drop cluster 19 and 7, these got roped in here probably because they are DATPs that express some collagen or proliferative epithelium. One is almost certainly AT2 (ABCA3/KRT8/ITGB6 positive) and the other is proliferative epithelial. 

```{r}
ipf3.fib.xen <- subset(x = ipf3.fib.xen, idents = c(7,19), invert = TRUE)
```
## Recluster after pruning
```{r}
ipf3.fib.xen <- NormalizeData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- FindVariableFeatures(ipf3.fib.xen, verbose=F) # defaults to finding 2000, which would be all of them, but downstream expects this.
ipf3.fib.xen <- ScaleData(ipf3.fib.xen, verbose=F)
ipf3.fib.xen <- RunPCA(ipf3.fib.xen, npcs = 30, features = rownames(ipf3.filtered.xen), verbose=F)
ipf3.fib.xen <- RunUMAP(ipf3.fib.xen, dims = 1:30, verbose=F)
```
```{r}
ipf3.fib.xen <- FindNeighbors(ipf3.fib.xen, reduction='pca', dims=1:30, prune.SNN=0, verbose=F) 
ipf3.fib.xen <- FindClusters(ipf3.fib.xen, resolution = 1.5, verbose=F)
```
```{r}
DimPlot(ipf3.fib.xen, label=T)
```
```{r}
FeaturePlot(ipf3.fib.xen, features=c('COL1A1','CTHRC1','POSTN','SCN7A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('INMT','RGS5','MYH11','CNN1'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('SFRP2','PLA2G2A','MFAP5','PI16'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('LGR5','WNT5A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('CCNA1','CCNB2','CDK1','CENPF'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('KIT','MKI67','PCNA','TOP2A'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('LTBP2','COL5A2','COL8A1','DPP6'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('APOD','MFAP5','FBN1','FGFR4'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('PCOLCE2','PDGFRA','SFRP2','SVEP1'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('WNT2','MEDAG','PIM1','RARRES1'),
            min.cutoff='q25', max.cutoff='q90', order=T)
FeaturePlot(ipf3.fib.xen, features=c('THBS2'),
            min.cutoff='q25', max.cutoff='q90', order=T)
```
```{r, fig.width=7, fig.height=6}
p <- DotPlot(ipf3.fib.xen, features=unique(c('EPCAM','ITGB6',
                                      'COL1A1','CTHRC1','POSTN','COL8A1','SCN7A','INMT','RGS5','MYH11','CNN1','SFRP2',
                                      'PLA2G2A','MFAP5','PI16','LGR5','WNT5A','CCNA1','CCNB2','CDK1','CENPF',
                                      'KIT','MKI67','PCNA','TOP2A','LTBP2','COL5A2','COL8A1','DPP6',
                                      'APOD','MFAP5','FBN1','FGFR4','PCOLCE2','PDGFRA','SFRP2','SVEP1',
                                      'WNT2','MEDAG','PIM1','RARRES1','THBS2'))
)
p + coord_flip() + scale_x_discrete(limits = rev)
```
```{r}
ipf3.fib.xen <- RenameIdents(object = ipf3.fib.xen,
                             `0` = "SmoothMuscle",
                             `1` = "AlveolarFib",
                             `2` = "FibroticFib_CTHRC1low",
                             `3` = "Pericyte",
                             `4` = "LipoFib",
                             `5` = "SmoothMuscle",
                             `6` = "AlveolarFib",
                             `7` = "FibroticFib_CTHRC1high",
                             `9` = "ProliferatingFib",
                             `8` = "AdventitialFib",
                             `10` = "AlveolarFib",
                             `11` = "Pericyte",
                             `12` = "AlveolarFib",
                             `13` = "AlveolarFib"
                             )
ipf3.fib.xen$named_clusters <- Idents(ipf3.fib.xen)
DimPlot(ipf3.fib.xen, label=T)
```
## Marker learning
On the theory that there might be hidden markers of utility in the Xenium probeset that we could co-opt for next cycle.
```{r}
fib.markers <- FindAllMarkers(ipf3.fib.xen, only.pos=T)
```

```{r}
fib.markers %>% group_by(cluster) %>% slice_head(n=10) %>% ungroup() -> top10
top10
```
```{r, fig.width=8, fig.height=7}
# manually save this
p1 <- DoHeatmap(ipf3.fib.xen, features = top10$gene) + NoLegend()
ggsave('forChris-proliferation-on-0020227_files/figure-gfm/markers-heatmap.png', units='in', width=9, height=8)
```
## Generate assignments file for Xenium Explorer

For version 1.2.0, the instructons say:

“Visualize custom cells groups in Xenium Explorer by importing a CSV file specifying cell ids and corresponding group names. Build a CSV file containing each cell id in the first column and the group the cell should be assigned to in the second column. A cell may only be assigned to one group. Not all cells in the dataset need to be included.

Ensure the columns have headers named “cell_id” and “group”.”

This is basically the named_clusters list with a header and saved as a CSV.

```{r}
scratch <- tibble(cell_id = names(ipf3.fib.xen$named_clusters),
                      group = ipf3.fib.xen$named_clusters)
write.csv(scratch, 'Seurat_fib_named_clusters.csv')
```
